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  <h3><a href="../contents.html">Table of Contents</a></h3>
  <ul>
<li><a class="reference internal" href="#">Statistics</a><ul>
<li><a class="reference internal" href="#order-statistics">Order statistics</a></li>
<li><a class="reference internal" href="#averages-and-variances">Averages and variances</a></li>
<li><a class="reference internal" href="#correlating">Correlating</a></li>
<li><a class="reference internal" href="#histograms">Histograms</a></li>
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  <div class="section" id="statistics">
<h1>Statistics<a class="headerlink" href="#statistics" title="Permalink to this headline">¶</a></h1>
<div class="section" id="order-statistics">
<h2>Order statistics<a class="headerlink" href="#order-statistics" title="Permalink to this headline">¶</a></h2>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
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<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.amin.html#numpy.amin" title="numpy.amin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">amin</span></code></a>(a[, axis, out, keepdims, initial, where])</p></td>
<td><p>Return the minimum of an array or minimum along an axis.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.amax.html#numpy.amax" title="numpy.amax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">amax</span></code></a>(a[, axis, out, keepdims, initial, where])</p></td>
<td><p>Return the maximum of an array or maximum along an axis.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.nanmin.html#numpy.nanmin" title="numpy.nanmin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanmin</span></code></a>(a[, axis, out, keepdims])</p></td>
<td><p>Return minimum of an array or minimum along an axis, ignoring any NaNs.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.nanmax.html#numpy.nanmax" title="numpy.nanmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanmax</span></code></a>(a[, axis, out, keepdims])</p></td>
<td><p>Return the maximum of an array or maximum along an axis, ignoring any NaNs.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.ptp.html#numpy.ptp" title="numpy.ptp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptp</span></code></a>(a[, axis, out, keepdims])</p></td>
<td><p>Range of values (maximum - minimum) along an axis.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.percentile.html#numpy.percentile" title="numpy.percentile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">percentile</span></code></a>(a, q[, axis, out, …])</p></td>
<td><p>Compute the q-th percentile of the data along the specified axis.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.nanpercentile.html#numpy.nanpercentile" title="numpy.nanpercentile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanpercentile</span></code></a>(a, q[, axis, out, …])</p></td>
<td><p>Compute the qth percentile of the data along the specified axis, while ignoring nan values.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.quantile.html#numpy.quantile" title="numpy.quantile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantile</span></code></a>(a, q[, axis, out, overwrite_input, …])</p></td>
<td><p>Compute the q-th quantile of the data along the specified axis.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.nanquantile.html#numpy.nanquantile" title="numpy.nanquantile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanquantile</span></code></a>(a, q[, axis, out, …])</p></td>
<td><p>Compute the qth quantile of the data along the specified axis, while ignoring nan values.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="averages-and-variances">
<h2>Averages and variances<a class="headerlink" href="#averages-and-variances" title="Permalink to this headline">¶</a></h2>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.median.html#numpy.median" title="numpy.median"><code class="xref py py-obj docutils literal notranslate"><span class="pre">median</span></code></a>(a[, axis, out, overwrite_input, keepdims])</p></td>
<td><p>Compute the median along the specified axis.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.average.html#numpy.average" title="numpy.average"><code class="xref py py-obj docutils literal notranslate"><span class="pre">average</span></code></a>(a[, axis, weights, returned])</p></td>
<td><p>Compute the weighted average along the specified axis.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.mean.html#numpy.mean" title="numpy.mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean</span></code></a>(a[, axis, dtype, out, keepdims])</p></td>
<td><p>Compute the arithmetic mean along the specified axis.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.std.html#numpy.std" title="numpy.std"><code class="xref py py-obj docutils literal notranslate"><span class="pre">std</span></code></a>(a[, axis, dtype, out, ddof, keepdims])</p></td>
<td><p>Compute the standard deviation along the specified axis.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.var.html#numpy.var" title="numpy.var"><code class="xref py py-obj docutils literal notranslate"><span class="pre">var</span></code></a>(a[, axis, dtype, out, ddof, keepdims])</p></td>
<td><p>Compute the variance along the specified axis.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.nanmedian.html#numpy.nanmedian" title="numpy.nanmedian"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanmedian</span></code></a>(a[, axis, out, overwrite_input, …])</p></td>
<td><p>Compute the median along the specified axis, while ignoring NaNs.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.nanmean.html#numpy.nanmean" title="numpy.nanmean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanmean</span></code></a>(a[, axis, dtype, out, keepdims])</p></td>
<td><p>Compute the arithmetic mean along the specified axis, ignoring NaNs.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.nanstd.html#numpy.nanstd" title="numpy.nanstd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanstd</span></code></a>(a[, axis, dtype, out, ddof, keepdims])</p></td>
<td><p>Compute the standard deviation along the specified axis, while ignoring NaNs.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.nanvar.html#numpy.nanvar" title="numpy.nanvar"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanvar</span></code></a>(a[, axis, dtype, out, ddof, keepdims])</p></td>
<td><p>Compute the variance along the specified axis, while ignoring NaNs.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="correlating">
<h2>Correlating<a class="headerlink" href="#correlating" title="Permalink to this headline">¶</a></h2>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.corrcoef.html#numpy.corrcoef" title="numpy.corrcoef"><code class="xref py py-obj docutils literal notranslate"><span class="pre">corrcoef</span></code></a>(x[, y, rowvar, bias, ddof])</p></td>
<td><p>Return Pearson product-moment correlation coefficients.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.correlate.html#numpy.correlate" title="numpy.correlate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">correlate</span></code></a>(a, v[, mode])</p></td>
<td><p>Cross-correlation of two 1-dimensional sequences.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.cov.html#numpy.cov" title="numpy.cov"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cov</span></code></a>(m[, y, rowvar, bias, ddof, fweights, …])</p></td>
<td><p>Estimate a covariance matrix, given data and weights.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="histograms">
<h2>Histograms<a class="headerlink" href="#histograms" title="Permalink to this headline">¶</a></h2>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.histogram.html#numpy.histogram" title="numpy.histogram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></a>(a[, bins, range, normed, weights, …])</p></td>
<td><p>Compute the histogram of a set of data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.histogram2d.html#numpy.histogram2d" title="numpy.histogram2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram2d</span></code></a>(x, y[, bins, range, normed, …])</p></td>
<td><p>Compute the bi-dimensional histogram of two data samples.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.histogramdd.html#numpy.histogramdd" title="numpy.histogramdd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogramdd</span></code></a>(sample[, bins, range, normed, …])</p></td>
<td><p>Compute the multidimensional histogram of some data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.bincount.html#numpy.bincount" title="numpy.bincount"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bincount</span></code></a>(x[, weights, minlength])</p></td>
<td><p>Count number of occurrences of each value in array of non-negative ints.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.histogram_bin_edges.html#numpy.histogram_bin_edges" title="numpy.histogram_bin_edges"><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram_bin_edges</span></code></a>(a[, bins, range, weights])</p></td>
<td><p>Function to calculate only the edges of the bins used by the <a class="reference internal" href="generated/numpy.histogram.html#numpy.histogram" title="numpy.histogram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></a> function.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.digitize.html#numpy.digitize" title="numpy.digitize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">digitize</span></code></a>(x, bins[, right])</p></td>
<td><p>Return the indices of the bins to which each value in input array belongs.</p></td>
</tr>
</tbody>
</table>
</div>
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